Within the class of machine learning methods to surrogate- and data-driven modeling, deep learning (DL) approaches have tasted great success because of their ability to efficiently extract complex relationships present in the underlying data. DL models have been successfully employed for a range of tasks in diverse fields such as material science, computational mechanics, computer vision, etc., [1]}, [2]}, [3]}, [4]}. In particular, also solid mechanics has witnessed an increasing use of DL surrogate models as a replacement to costly direct numerical simulations [5]}, [6]}. Deep learning techniques rely on deep artificial neural networks (deep ANNs, or DNNs). DNNs need to be trained on a sufficiently large amount of data, which is a computationally costly process. However, once trained, the predictions of DL models are computationally extremely efficient.
